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Intelligent learning technique based-on fuzzy logic for multi-robot path planning

Journal of Harbin Institute of Technology(New Series) 2001年第3期8卷 222-227页

作者：孟庆春殷波熊建设魏天滨王旭柱王汝霖 Dept. of Electronics Engineering，Ocean University of Qingdao State Key Lab. of Intelligent Technology Systems，Tsinghua University Dept. of Electronics Engineering，Ocean University of Qingdao

Soccer robot system is a tremendously challenging intelligent system developed to mimic human soccer competition based on the multi discipline research: robotics, intelligent control, computer vision, etc. robot path planning strategy is a very important subject concerning to the performance and intelligence degree of the multi robot system. Therefore, this paper studies the path planning strategy of soccer system by using fuzzy logic. After setting up two fuzziers and two sorts of fuzzy rules for soccer system, fuzzy logic is applied to workspace partition and path revision. The experiment results show that this technique can well enhance the performance and intelligence degree of the system.

关键词： fuzzy logic multi agent system intelligent system robot path planning

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Generative Adversarial Network Based Heuristics for Sampling-Based path planning

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IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 64-74页

作者： Tianyi Zhang Jiankun Wang Max Q.-H.Meng Department of Electronic and Electrical Engineering Southern University of Science and TechnologyShenzhen 518055China

Sampling-based path planning is a popular methodology for robot path planning.With a uniform sampling strategy to explore the state space,a feasible path can be found without the complex geometric modeling of the configuration space.However,the quality of the initial solution is not guaranteed,and the convergence speed to the optimal solution is slow.In this paper,we present a novel image-based path planning algorithm to overcome these limitations.Specifically,a generative adversarial network(GAN)is designed to take the environment map(denoted as RGB image)as the input without other preprocessing works.The output is also an RGB image where the promising region(where a feasible path probably exists)is segmented.This promising region is utilized as a heuristic to achieve non-uniform sampling for the path planner.We conduct a number of simulation experiments to validate the effectiveness of the proposed method,and the results demonstrate that our method performs much better in terms of the quality of the initial solution and the convergence speed to the optimal solution.Furthermore,apart from the environments similar to the training set,our method also works well on the environments which are very different from the training set.

关键词： Generative adversarial network(GAN) optimal path planning robot path planning sampling-based path planning

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An Enhanced Equilibrium Optimizer for Solving Optimization Tasks

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Computers, Materials & Continua 2023年第11期77卷 2385-2406页

作者： Yuting Liu Hongwei Ding Zongshan Wang Gaurav Dhiman Zhijun Yang Peng Hu School of Information Science and Engineering Yunnan UniversityKunming650500China Department of Electrical and Computer Engineering Lebanese American UniversityByblos1401Lebanon University Centre for Research and Development Department of Computer Science and EngineeringChandigarh UniversityGharuanMohali140413India Department of Computer Science and Engineering Graphic Era(Deemed to be University)Dehradun248002India Research and Development Department Youbei Technology Co.Ltd.Kunming650500China

The equilibrium optimizer(EO)represents a new,physics-inspired metaheuristic optimization approach that draws inspiration from the principles governing the control of volume-based mixing to achieve dynamic mass equilibrium.Despite its innovative foundation,the EO exhibits certain limitations,including imbalances between exploration and exploitation,the tendency to local optima,and the susceptibility to loss of population diversity.To alleviate these drawbacks,this paper introduces an improved EO that adopts three strategies:adaptive inertia weight,Cauchy mutation,and adaptive sine cosine mechanism,called SCEO.Firstly,a new update formula is conceived by incorporating an adaptive inertia weight to reach an appropriate balance between exploration and exploitation.Next,an adaptive sine cosine mechanism is embedded to boost the global exploratory capacity.Finally,the Cauchy mutation is utilized to prevent the loss of population diversity during searching.To validate the efficacy of the proposed SCEO,a comprehensive evaluation is conducted on 15 classical benchmark functions and the CEC2017 test suite.The outcomes are subsequently benchmarked against both the conventional EO,its variants,and other cutting-edge metaheuristic techniques.The comparisons reveal that the SCEO method provides significantly superior results against the standard EO and other competitors.In addition,the developed SCEO is implemented to deal with a mobile robot path planning(MRPP)task,and compared to some classical metaheuristic approaches.The analysis results demonstrate that the SCEO approach provides the best performance and is a prospective tool for MRPP.

关键词： Metaheuristic algorithms equilibrium optimizer Cauchy mutation robot path planning

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Adaptive pass planning and optimization for robotic welding of complex joints

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Advances in Manufacturing 2017年第2期5卷 93-104页

作者： H. C. Fang S. K. Ong A. Y. C. Nee Department of Mechanical Engineering Faculty ofEngineering National University of SingaporeSingapore 117575 Singapore

Current industrial robotic welding systems can- not achieve automated solutions for multi-layer multi-pass welding of complex joints due to the presence of non- uniform and irregular welding groove geometries. This paper presents an adaptive pass planning approach for robotic welding of such complex joints. The welding groove is first segmented considering both the variation in groove dimension and the reachability of the robot welding torch. For each welding segment, the welding passes are planned to be in accordance with welding practices, viz., keeping the same number of welding passes in each layer while maintaining consistent welding parameters. An adaptive pass adjustment scheme is developed to address the discrepancies between the simulated results and the actual welding deposition after finishing a few layers of welding. Corresponding robot paths are generated and optimized to ensure minimum joint movement subject to three constraints, viz., reachability, collision-free and singularity avoidance. The proposed approach has been sim- ulated with the arc welding of a Y-type joint found typically in offshore structures.

关键词： robotic welding Multi-pass planning - Pass adjustment robot path planning

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Adaptive Cockroach Colony Optimization for Rod-Like robot Navigation

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Journal of Bionic Engineering 2015年第2期12卷 324-337页

作者： Le Cheng Lixin Han Xiaoqin Zeng Yuetang Bian Hong Yan College of Computer and Information Hohai University Nanjing 210098 China Department of Computer Science and Engineering Huaian College of Information Technology China School of Business Nanjing Normal University Nanjing 210023 China School of Electrical and Information Engineering University of Sydney NSW 2006 Australia ~ Department of Electronic Engineering City University ofHong Kong Tat Chee Avenue Kowloon Hong Kong China

Based on the Cockroach Swarm Optimization （CSO） algorithm, a new Cockroach Colony Optimization （CCO） algorithm is presented and applied to the robot path planning （RPP） problem in this paper. In the CCO algorithm, an improved grid map is used for environment modeling, and 16-geometry and 8-geometry are introduced, respectively, in food division and cockroach search operation. Moreover, the CCO algorithm adopts a non-probabilistic search strategy, which avoids a lot of invalid searches. Furthermore, by introducing a novel rotation scheme in the above CCO algorithm, an Adaptive Cockroach Colony Optimization （ACCO） algorithm is presented for the 2-D Rod-Like robot path planning （RLRPP） problem. The simulation results show that the CCO algorithm can plan an optimal or approximately optimal collision-free path with linear time com- plexities. With the ACCO algorithm, the robot can accomplish intelligent and adaptive rotations to avoid obstacles and pass through narrow passages along the better path.

关键词： CCO robot path planning adaptive CCO rod-like robot path planning piano mover＇s problem

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Narrow Passages Identification for Probabilistic Roadmap Method

Narrow Passages Identification for Probabilistic Roadmap Met...

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第三十届中国控制会议

作者： ZHONG Jiandong,SU Jianbo Department of Automation,Shanghai Jiao Tong University,Shanghai,200240,P.R.China

＜正＞With narrow passages in robot working space,the Probabilistic Roadmap Method（PRM） is hard to arrive at an efficient path due to unreasonable milestones in limited space.This paper presents a hybrid sampling strategy in the PRM framework to improve the distributions of road signs.It proposes the Randomized Star Builder（RSB） to identify narrow passages in the workspace,and uses the hybrid strategy to sample road signs in the corresponding configuration space.The density of points in the roadmap is then increased in the narrow passages.Moreover,global roadmaps using Uniform Sampler are also configured to rationalize the milestone distribution so as to improve the path planning efficiency.A robot system of multiple degree-of-freedoms is exemplified in simulations to show the effectiveness of the proposed algorithm.

关键词： robot path planning Narrow passages Probabilistic Roadmap Method Roadmap sampling Configuration space

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